About this Research Topic
Therefore, as AI technology becomes more pervasive, it is crucial to address the challenges associated with deploying AI in safety-critical systems. These systems must adhere to stringent safety requirements to ensure the well-being of individuals and the environment.
Despite the great success of AI, the use of Deep Learning models presents new dependability challenges, such as lack of well-defined specification, black-box nature of the models, high-dimensionality of data and over-confidence of neural networks over out-of-distribution data.
Therefore, to cope with such issues, a new topic emerged: AI Safety. AI Safety is a multidisciplinary domain that lies at the intersection between AI, Software Engineering, Safety Engineering and Ethics, and is an essential and challenging topic that aims at improving the safety and provide certifiably safety-critical autonomous systems powered by AI solutions. It involves mitigating risks associated with AI failures, ensuring the robustness and resilience of AI algorithms, enabling human-AI collaboration and addressing ethical concerns in critical domains.
This Research Topic aims to gather cutting-edge research, insights, and methodologies in the field of AI safety, focusing specifically on safety-critical systems. We invite original contributions in the form of research articles, survey papers, case studies and reviews that explore various aspects of AI safety for safety-critical systems.
The topics of interest include, but are not limited to:
• Risk assessment and management for AI in safety-critical systems
• Verification and validation techniques for AI-driven systems
• Explainability (interpretability) of AI models in safety-critical domains
• Robustness and resilience of AI algorithms and systems
• Human-AI interaction and collaboration in safety-critical settings
• Ethical considerations and responsible AI practices for safety-critical systems
• Regulatory frameworks and standards for AI safety in critical domains
• Case studies and practical applications of AI safety in real-world scenarios
Keywords: Artificial Intelligence, Deep Learning, Safety, Software Engineering, Safe AI
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.